1. Field of the Invention
This invention relates to a random error signal generator for generating random error signals having specified error rates, of which the number of error occurrence times follows Poisson distribution within a predetermined counting time period, and of which the occurrence time interval of adjacent two errors follows geometric distribution.
2. Description of the Related Art
There have been developed test apparatuses for performing various tests to sets of communication equipment incorporated in digital communication networks using generic electric signal cables or optical communication networks using optical fiber cables. These test apparatuses input test signals matching with actual use situations of the communication equipment to he tested to evaluate response operations of the communication equipment. In terms of one of evaluation tests to such communication equipment, there is an evaluation test of generating a test signal to which errors are intentionally included as a test signal which matches with an actual use situation and sending the test signal to the communication equipment to be measured. In the evaluation test, a maximum error rate that the communication equipment works well is examined.
Origin of errors in various digital communication networks such as connecting among generic user terminal and a base station, or connecting among subscriber terminals and a telephone station, etc. is manifold In a network connecting among base station mutually, errors are mainly due to external noise. Usually, an error occurrence rate (error rate) p included in the digital signal is in the order of p=10−2 to 10−8, and also these errors are generated at random.
If the word “random” is expressed in different word “unpredictable”, the number of errors occurring in a predetermined counting time is unpredictable and a time interval from an occurrence of one error to an occurrence of the next error (referred to as an error occurrence interval) is also unpredictable. The former property concerning the number of times of the error occurrences is referred to as unpredictability of a counting property, and the latter property concerning the error occurrence interval is referred to as unpredictability of an interval property.
Therefore, as shown in FIG. 14, a test apparatus 1 includes a random error signal generator 3 other than a test signal generator circuit 2 generating an original digital test signal a. The random error signal generator 3 generates a random error signal b in which, errors, for example, of [1] bit occur at a specified error occurrence rate (error rate) p and also at random. An exclusive OR gate 4 applies exclusive OR arithmetic operation to the digital test signal output from the generation circuit 2 and the random error signal b output from the random error signal generator 3, an inverter 5 inverts he result of the arithmetic operation to generate a test signal a1 including errors at the specified error occurrence rate p.
Statistic properties of the errors occurred in the random error signal will be verified by using a probability theory hereinafter. That is, in the random error signal, it is impossible to determine whether or not errors occur at one clock cycle TC. However, an occurrence probability of errors in the random error signal is set to a fixed value p. A random error occurrence circuit for generating such random error signals may be assumed to be a device which repeats independent Bernoulli Trials of a population parameter (occurrence rate) p at a certain fixed cycle by using a term of the probability theory.
The Bernoulli Trials of the population parameter p are trials of a probability of a success of p(0<p<1), and of a probability of a failure of q=(1−p). The Bernoulli Trials output [1] when they success, and output [0] when they fail. The word “independent” means that the results of the respective trials do not affect on the results from other than them (results of other trials).
When the Bernoulli Trials of the population parameter p are repeated, the number of trial times from the [1] is output until the [1] will be output next follows a probability distribution and the distribution may be expressed in a geometric distribution. That is, the geometric distribution is equivalent to a probability distribution obtained by repeating the Bernoulli Trials of the population parameter (probability) p(0<p<1), and a probability P (j, p) in which the number of trials from the success (output [1]) to the next success (output [1]) becomes j is expressed by the following equation.p(j, p)=qj×p j=0, 1, 2, 3, . . .
wherein, q=1−p0<q<1
In this way, the distribution of error intervals of the respective errors (interval from one error occurrence up to an occurrence of the next error) included in the random error signal follows the geometric distribution in theory.
Thus, the following two view points evaluate whether or not the respective errors included in the random error signals may be assumed to actually occur at random.
(a) The number of times of error occurrences in a predetermined counting time follows a binomial distribution. Evaluation of the random error signals from this point of view means to examine the counting property of the errors as mentioned previously. However, since the foregoing binomial distribution gradually approaches to Poisson distribution at a limit in which a counting time is fully long and the error rate p is fully small, if the binomial distribution gradually approaches to Poisson distribution, the number of times of the error occurrences may be assumed to follow Poisson distribution. Hereinafter, the following of the number of times of the error occurrences within the predetermined counting time to Poisson distribution is referred to as a satisfaction of the counting property by the random error signal. This Poisson distribution P(k, λ), the probability that a random variable takes a value k, is generally expressed by the following equation.P(k, λ)=(e−λ×λk)/k!
wherein λ; averaged value
(b) An interval between an occurrence time of a certain error and an occurrence time of another error (for simplification, this amount is referred to as error occurrence interval) follows a predetermined distribution. Especially, be interval between two adjacent errors should follow the foregoing geometric distribution. Evaluation of a random property of the random error signal from this point of view means to examine the interval property of the errors as mentioned above. Hereinafter, the following of the adjacent errors occurrence interval with a geometric distribution is referred to as the satisfaction of the interval property by the random signal.
An example of an error signal generation circuit generating the random error signals, of which the number of times of error occurrences within the predetermined counting time mentioned in the (a) follows Poisson distribution, is described in Jpn. Pat. Appln. KOKAI publication No. 2002-330192. However, a detailed configuration of such a random error signal generator is not described in Jpn. Pat. Appln. KOKAI publication No. 2002-330192, it may be estimated, from the specification and the drawings, that the random error signal generator 3 has a configuration to be shown in FIGS. 15 and 16.
As an example is shown in FIG. 16, an M-sequence generation circuit 6 shown in FIG. 15 is composed of registers 7 serially connected in a manner of m stages and one or more exclusive OR gates 8. When an external clock circuit 9 applies a clock CLK to each register 7, the M-sequence generation circuit 6 outputs pseudo random signals that are digital serial signals having a cycle of (2m−1) from an output terminal 10.
At every input of the clock CLK, each piece of bit data (pseudo random binary sequence) stored in each register 7 is output in parallel with one another. Each piece of bit data output from the M-sequence generation circuit 6 in parallel with one another is applied to one input terminal (X terminal) of a comparator 11. Reference values of parallel m-bit input by an operator through a reference value generation circuit 12 are input to the other input terminal (Y terminal).
The comparator 11 loads, as one numeric value A, paralleled m pieces of bit data applied to one input terminal (X terminal). Similarly, the comparator 11 loads, as one numeric value, a reference value B of paralleled m-bit applied to the other input terminal (Y terminal). If the numeric value A loaded from one input terminal (X terminal is not larger than the reference value B loaded from the other input terminal (Y terminal), the comparator 11 outputs a random error signal b to be an error bit.
The reference value B is set so that the random error signal b output from the random error signal generator 3 becomes the error occurrence rate (error rate) p to be targeted. As given above, since the numeric value A takes a integer value one or more and less than (2m−1) only each one time in one cycle, to set the error rate to p, the reference value B is set to an integer value that is closest to (2m−1)×p.
For instance, in a case where the error rate E is 0.004 (0.4%) , and the integer value which can be taken from the X terminal is 1 to 1,000, the reference value B is set to [4]. Since the numeric value A becoming smaller than [4] has a probability of 4/1,000, the random error signal b having the error rate E of 0.004 may be obtained.
The random error signal generators 3 shown in FIGS. 15 and 16 still have the following problems to be solved. That is, the error occurrence rate (error rate) of the random error signal b output from the random error signal generator 3 is able to conform the error occurrence rate (error rate) of the random error signal b to the accurately specified error occurrence rate (error rate) p. However, the generated random error signals do not satisfy the counting property shown in the foregoing Poisson distribution and the interval property shown in the geometric distribution. Referring now to FIG. 17, the reason why the random error signals do not satisfy the interval property will be described hereinafter.
FIG. 17 shows a time chart showing operations of the random error signal generator 3 shown in FIGS. 15 and 16. However, in FIG. 17, the number of stages m of the registers 7 configuring the shift register shown in FIG. 16 is described as 10(m=10) As shown at (a) of FIG. 17, when one clock CLK is input, the pseudo random signal is output from the output terminal 10 of the M-sequence generation circuit 6 as shown at (b) of FIG. 17. As shown at (B) of FIG. 17, if a part of the pseudo random signal is, for example,[. . . , 0001000000000, . . .], and if the paralleled m pieces (bits) (m=10) of data output from each register is [1000000000], the numeric value A of the m pieces (bits) of data is [1]. As shown at (c) of FIG. 17, if the reference value B is [4], since the numeric A is less than the reference value B, the output from the comparator 11 becomes an error bit of [1] as shown at (d) of FIG. 17.
As shown at (a) of FIG. 17, when the next clock CLK is input, since the data of each register 7 shifts one by one, the paralleled m(=10) pieces of data becomes [0100000000]. The numeric value A of this m(=10) pieces (bits) of data is [2] as shown at (b) of FIG. 17. As shown in FIG. 17 (c), since the reference B is fixed to [4], the output from the comparator 11 is still remained at an error bit of [1] as shown at (d) of FIG. 17.
Further, as shown at (a) of FIG. 17, when the next clock CLK is input, since the data of each register 7 shifts one by one, the paralleled m(=10) pieces (bits) of data becomes [0010000000] as shown at (b) of FIG. 17, and the numeric value A of the m(=10) pieces (bits) is equivalent to [4]. As shown at (c) of FIG. 17, since the reference value B is [4], the output from the comparator 11 remains the error bit of [1] as shown at (d) of FIG. 17
Further, as shown at (a) of FIG. 17, when the next clock CLK is input, since the data of each register 7 shifts one by one, the paralleled m(=10) pieces (bits) of data becomes [0001000000], and the numeric value (data numeric value) A of the data of this m(=10) pieces (bits) is equivalent to [8]. As shown at (c) of FIG. 17, since the reference value B is [4], the output from the comparator 11 changes into a normal bit of [0] as shown at (d) of FIG. 17.
In this way, solely the case in which at least one of each register 7 connected to an exclusive OR gate 8 is equivalent to a value [1] breaks the shift relationship between each register 7, and otherwise the shift relationship is reserved long in synchronization with the clock CLK. Therefore, if the error bit of [1] is generated once, there is every possibility of generating the error bits of [1] successively. Then, the normal bits of [0] continue for a long time.
This means the error distribution of the random error signal b output from the random error signal generator 3 concentrates to a specified time position, and this results in large deflection from Poisson distribution. That is, a problem is posed, which the random error signal b output from the random error signal generator 3 does not satisfy the counting property. The random error signal generator 3 also poses a problem that the distribution of the adjacent error occurrence intervals showing the interval between a certain error occurrence time and an occurrence time of another error largely deflected from the geometric distribution and that the random error signal does not satisfy the interval property.